Machine learning methods and systems for managing retail store processes involving cashier-less transactions
First Claim
1. A method for identifying actions in a retail store, comprising:
- (a) sampling a shopping environment using one or more sensors that include at least one camera capable of providing depth sensing to produce image data of a scene that shows a shopper in the retail store and tracking data related to one or more limbs of the shopper in connection to an item;
(b) receiving output of the sampling as feature inputs to one or more machine learning classifier models to derive one or more labels characterizing a behavior state of the shopper in connection with a state of the item; and
(c) wherein at least one processing entity associated with the retail store detects the state of the item to change from one as item taken to one as item returned, and sensor data from said one or more sensors used to produce one or more labels that indicate the item as having been returned to a wrong location in the retail store.
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Abstract
Devices, systems, and method are provided for identifying actions in a retail store. One method includes sampling a shopping environment using one or more sensors. The method includes receiving output of the sampling as feature inputs to a machine learning classifier model to derive one or more labels characterizing a state of an item. At least one processing entity associated with the retail store detects the state of the item to change from one as item taken to one as item returned, and sensor data from said one or more sensors characterizing the item as having been returned to a wrong location in the retail store. In further embodiments, items taken are added to a user'"'"'s electronic shopping cart to enable processing of a cashier-less transaction.
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Citations
25 Claims
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1. A method for identifying actions in a retail store, comprising:
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(a) sampling a shopping environment using one or more sensors that include at least one camera capable of providing depth sensing to produce image data of a scene that shows a shopper in the retail store and tracking data related to one or more limbs of the shopper in connection to an item; (b) receiving output of the sampling as feature inputs to one or more machine learning classifier models to derive one or more labels characterizing a behavior state of the shopper in connection with a state of the item; and (c) wherein at least one processing entity associated with the retail store detects the state of the item to change from one as item taken to one as item returned, and sensor data from said one or more sensors used to produce one or more labels that indicate the item as having been returned to a wrong location in the retail store. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method of identifying an event of a shopper taking an item in a store where the item taken is automatically identified, comprising:
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(a) sampling a shopping environment using one or more sensors to include a reach in toward the item by a limb of the shopper; (b) using a processing entity associated with the store to process an output of the sampling to identify the item taken by the shopper based on the reach in updating a state of the item that is labeled using processed features derived form the sampling, (c) using a processing entity associated with the store to process the output of the sampling to identify the item put down by the shopper based on further sampling in (a) that includes the put down and processing in (b) for updating the state of the item, (d) using a processing entity associated with the store to process the output of the sampling to identify the item put down by the shopper as being put in a wrong location in the store. - View Dependent Claims (13, 14, 15)
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16. A method for identifying actions in a store, comprising:
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capturing data in a shopping environment using one or more sensors including a camera to include a reach in toward an item by a limb of a shopper; receiving output of the capturing as feature inputs to a machine learning classifier model to derive one or more labels characterizing a state of the item; processing by at least one processing entity associated with the store to detect a change in the state of the item that indicates a take of the item from a location of the store based on said one or more labels used to identify the reach in as associated with the state of the item; associating the take of the item to a user account of the shopper based on presence of a user device of the shopper at the store; and charging the item to an electronic shopping cart for the user account upon confirming that the user device of the shopper has exited the store with the item. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25)
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Specification